When compared with other GBDT libraries, CatBoost results in Superior quality.

Known to be Best, when it comes to class inference speed.

Provides Support for both numerical as well as categorical features.

For the purpose of training, Fast GPU and multi-GPU (on one node) support.

The Inclusion of Data visualization tools.

More Insight into CatBoost:

When it comes to the version 0.6 of CatBoost, it provides itself with a lot of speedups as well as improvements, The most valuable improvement at the moment being the release of industry fastest inference implementation.

We can surely say that CatBoost is the Best when we talk of class inference as well as a ton of speedups.

Moving on further, CatBoost makes the use of oblivious trees as base predictors. Each leaf index in oblivious trees can be encoded as a binary vector where the length of the tree is equal it's depth. This fact is widely made use of in CatBoost model evaluator:

We first binarize all used float features, statistics and one-hot encoded features.

Then, after the same make use of binary features in order to calculate model predictions.

Thee vectors can be built in a data-parallel manner with SSE intrinsics.

Speedups of the Open-source method:

The team of CatBoost has spent a lot of effort when it comes to the speedup of different parts of library. As of now, the list is below:

When training on large datasets, a speedup of 43%.

For the purpose of QueryRMSE and calculation of query wise metrics, a speedup of 15%.